{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster Analysis\n",
    "\n",
    "Data reduction in statistics and machine learning: factor analysis, principle components analysis, cluster analysis\n",
    " - in statistics, several observed variables might represent a single latent variable (reduce number of dimensions)\n",
    " - in spatial analysis, several lat-long points may represent a single \"place\" (reduce number of examples)\n",
    "\n",
    "Cluster analysis algorithms\n",
    " - *k-means* partitions the data space into Voronoi tessellations. Translation: you give it a desired number of clusters and it will partition them into equal-sized clusters (minimizes variance and fails at density-based clustering).\n",
    " - *DBSCAN* discovers clusters as dense areas in space, surrounded by sparser areas. Points in the sparse areas are usually considered noise (needs there to be drop-offs in density to detect cluster borders).\n",
    " - *OPTICS* is similar to DBSCAN, but lets you find clusters of varying density.\n",
    " - many more...\n",
    "\n",
    "DBSCAN is appropriate for geospatial data (when using haversine/great-circle distances or projected data), and we'll focus on it today using scikit-learn.\n",
    "\n",
    "- http://scikit-learn.org/stable/modules/clustering.html\n",
    "- http://scikit-learn.org/stable/modules/clustering.html#dbscan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# magic command to display matplotlib plots inline within the ipython notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# import necessary modules\n",
    "import pandas as pd, numpy as np, matplotlib.pyplot as plt, time\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn import metrics\n",
    "from geopy.distance import great_circle\n",
    "from shapely.geometry import MultiPoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define the number of kilometers in one radian\n",
    "kms_per_radian = 6371.0088"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: spatial clustering into groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.481292</td>\n",
       "      <td>-0.451011</td>\n",
       "      <td>05/14/2014 09:07</td>\n",
       "      <td>West Drayton</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.474005</td>\n",
       "      <td>-0.450999</td>\n",
       "      <td>05/14/2014 09:22</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.478199</td>\n",
       "      <td>-0.446081</td>\n",
       "      <td>05/14/2014 10:51</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.478199</td>\n",
       "      <td>-0.446081</td>\n",
       "      <td>05/14/2014 11:24</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.474146</td>\n",
       "      <td>-0.451562</td>\n",
       "      <td>05/14/2014 11:38</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat       lon              date          city         country\n",
       "0  51.481292 -0.451011  05/14/2014 09:07  West Drayton  United Kingdom\n",
       "1  51.474005 -0.450999  05/14/2014 09:22      Hounslow  United Kingdom\n",
       "2  51.478199 -0.446081  05/14/2014 10:51      Hounslow  United Kingdom\n",
       "3  51.478199 -0.446081  05/14/2014 11:24      Hounslow  United Kingdom\n",
       "4  51.474146 -0.451562  05/14/2014 11:38      Hounslow  United Kingdom"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the data set\n",
    "df = pd.read_csv('data/summer-travel-gps-full.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1759"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many rows are in this data set?\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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uY0Txmg2N123wvGZD43UbnJqfnbsOx+dFSmk4PmdYRcQPgRXAucAG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x236e81619b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatterplot it to get a sense of what it looks like\n",
    "df = df.sort_values(by=['lat', 'lon'])\n",
    "ax = df.plot(kind='scatter', x='lon', y='lat', alpha=0.5, linewidth=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute DBSCAN\n",
    "\n",
    "The scikit-learn DBSCAN haversine distance metric requires data in the form of [latitude, longitude] and both inputs and outputs are in units of radians.\n",
    "\n",
    "- eps is the physical distance from each point that forms its neighborhood\n",
    "- min_samples is the min cluster size, otherwise it's noise - set to 1 so we get no noise\n",
    "\n",
    "Extract the lat, lon columns into a numpy matrix of coordinates, then convert to radians when you call fit, for use by scikit-learn's haversine metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# represent points consistently as (lat, lon)\n",
    "coords = df.as_matrix(columns=['lat', 'lon'])\n",
    "\n",
    "# define epsilon as 10 kilometers, converted to radians for use by haversine\n",
    "epsilon = 10 / kms_per_radian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "start_time = time.time()\n",
    "db = DBSCAN(eps=epsilon, min_samples=10, algorithm='ball_tree', metric='haversine').fit(np.radians(coords))\n",
    "cluster_labels = db.labels_\n",
    "unique_labels = set(cluster_labels)\n",
    "\n",
    "# get the number of clusters\n",
    "num_clusters = len(set(cluster_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dczsJKRP4wQ9+0Cuurn4OGzdu5MCBAyxbtozZs2cTCASor6/HarVy8uRJ2tra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\nVJAmBUoppZQCNClQSimlVND/D+T0K+0hbEyrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x236e8a59518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get colors and plot all the points, color-coded by cluster (or gray if not in any cluster, aka noise)\n",
    "fig, ax = plt.subplots()\n",
    "colors = plt.cm.rainbow(np.linspace(0, 1, len(unique_labels)))\n",
    "\n",
    "# for each cluster label and color, plot the cluster's points\n",
    "for cluster_label, color in zip(unique_labels, colors):\n",
    "    \n",
    "    size = 150\n",
    "    if cluster_label == -1: #make the noise (which is labeled -1) appear as smaller gray points\n",
    "        color = 'gray'\n",
    "        size = 30\n",
    "    \n",
    "    # plot the points that match the current cluster label\n",
    "    x_coords = coords[cluster_labels==cluster_label][:,1]\n",
    "    y_coords = coords[cluster_labels==cluster_label][:,0]\n",
    "    ax.scatter(x=x_coords, y=y_coords, c=color, edgecolor='k', s=size, alpha=0.5)\n",
    "\n",
    "ax.set_title('Number of clusters: {}'.format(num_clusters))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib offers lots of built-in colormaps: http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "\n",
    "The silhouette coefficient evaluates how close a point is to the other points in its cluster in comparison with how close it is to the points in the next nearest cluster. A high silhouette coefficient indicates the points are well-clustered and a low value indicates an outlier.\n",
    "\n",
    "http://scikit-learn.org/stable/modules/clustering.html#silhouette-coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette coefficient: 0.854\n"
     ]
    }
   ],
   "source": [
    "coefficient = metrics.silhouette_score(coords, cluster_labels)\n",
    "print('Silhouette coefficient: {:0.03f}'.format(metrics.silhouette_score(coords, cluster_labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now you try: experiment with different epsilon values, minimum sizes, and colormaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: clustering to reduce data set size\n",
    "\n",
    "Rather than clustering to discover groups, I want to cluster to reduce the size of my data set. Even zoomed in very close, several locations have hundreds of data points stacked directly on top of each other due to the duration of time spent at one location. Unless we are interested in time dynamics, we simply do not need all of these spatially redundant points – they just bloat the data set’s size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustered 1,759 points down to 138 clusters, for 92.2% compression in 0.05 seconds\n"
     ]
    }
   ],
   "source": [
    "# set eps low (1.5km) so clusters are only formed by very close points\n",
    "epsilon = 1.5 / kms_per_radian\n",
    "\n",
    "# set min_samples to 1 so we get no noise - every point will be in a cluster even if it's a cluster of 1\n",
    "start_time = time.time()\n",
    "db = DBSCAN(eps=epsilon, min_samples=1, algorithm='ball_tree', metric='haversine').fit(np.radians(coords))\n",
    "cluster_labels = db.labels_\n",
    "unique_labels = set(cluster_labels)\n",
    "\n",
    "# get the number of clusters\n",
    "num_clusters = len(set(cluster_labels))\n",
    "\n",
    "# all done, print the outcome\n",
    "message = 'Clustered {:,} points down to {:,} clusters, for {:.1f}% compression in {:,.2f} seconds'\n",
    "print(message.format(len(df), num_clusters, 100*(1 - float(num_clusters) / len(df)), time.time()-start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette coefficient: 0.652\n"
     ]
    }
   ],
   "source": [
    "coefficient = metrics.silhouette_score(coords, cluster_labels)\n",
    "print('Silhouette coefficient: {:0.03f}'.format(metrics.silhouette_score(coords, cluster_labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters: 138\n"
     ]
    }
   ],
   "source": [
    "# number of clusters, ignoring noise if present\n",
    "num_clusters = len(set(cluster_labels)) #- (1 if -1 in labels else 0)\n",
    "print('Number of clusters: {}'.format(num_clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133                           [[50.3736895, 18.8892047]]\n",
       "134    [[50.4487039, 19.0594976], [50.4492979, 19.060...\n",
       "135                           [[50.4622715, 19.0815141]]\n",
       "136                           [[50.4893042, 19.0983815]]\n",
       "137    [[51.474005, -0.4509991], [51.4741456, -0.4515...\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a series to contain the clusters - each element in the series is the points that compose each cluster\n",
    "clusters = pd.Series([coords[cluster_labels == n] for n in range(num_clusters)])\n",
    "clusters.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the point in each cluster that is closest to its centroid\n",
    "\n",
    "DBSCAN clusters may be non-convex. This technique just returns one representative point from each cluster. First get the lat,lon coordinates of the cluster's centroid (shapely represents the first coordinate in the tuple as x and the second as y, so lat is x and lon is y here). Then find the member of the cluster with the smallest great circle distance to the centroid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# given a cluster of points, return the point nearest to the cluster's centroid\n",
    "def get_centermost_point(cluster):\n",
    "    centroid = (MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)\n",
    "    centermost_point = min(cluster, key=lambda point: great_circle(point, centroid).m)\n",
    "    return tuple(centermost_point)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geopy's great circle distance calculates the shortest distance between two points along the surface of a sphere. https://en.wikipedia.org/wiki/Great-circle_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>50.373690</td>\n",
       "      <td>18.889205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>50.449298</td>\n",
       "      <td>19.060560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>50.462271</td>\n",
       "      <td>19.081514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>50.489304</td>\n",
       "      <td>19.098381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>51.478199</td>\n",
       "      <td>-0.446081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           lat        lon\n",
       "133  50.373690  18.889205\n",
       "134  50.449298  19.060560\n",
       "135  50.462271  19.081514\n",
       "136  50.489304  19.098381\n",
       "137  51.478199  -0.446081"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the point in each cluster that is closest to its centroid\n",
    "centermost_points = clusters.map(get_centermost_point)\n",
    "\n",
    "# unzip the list of centermost points (lat, lon) tuples into separate lat and lon lists\n",
    "lats, lons = zip(*centermost_points)\n",
    "\n",
    "# from these lats/lons create a new df of one representative point for each cluster\n",
    "representative_points = pd.DataFrame({'lon':lons, 'lat':lats})\n",
    "representative_points.tail()\n",
    "representative_points.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My data set of 1,759 points has been reduced down to a spatially representative sample of 158 points. Last thing - grab the rows in the original dataframe that correspond to these 158 points (so that I keep city, country, and timestamp)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>50.373690</td>\n",
       "      <td>18.889205</td>\n",
       "      <td>06/02/2014 07:25</td>\n",
       "      <td>Bytom</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>50.449298</td>\n",
       "      <td>19.060560</td>\n",
       "      <td>05/30/2014 16:39</td>\n",
       "      <td>Tarnowskie Góry County</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>50.462271</td>\n",
       "      <td>19.081514</td>\n",
       "      <td>06/02/2014 07:10</td>\n",
       "      <td>Tarnowskie Góry County</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>50.489304</td>\n",
       "      <td>19.098381</td>\n",
       "      <td>05/30/2014 16:10</td>\n",
       "      <td>Zendek</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>51.478199</td>\n",
       "      <td>-0.446081</td>\n",
       "      <td>05/14/2014 10:51</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           lat        lon              date                    city  \\\n",
       "133  50.373690  18.889205  06/02/2014 07:25                   Bytom   \n",
       "134  50.449298  19.060560  05/30/2014 16:39  Tarnowskie Góry County   \n",
       "135  50.462271  19.081514  06/02/2014 07:10  Tarnowskie Góry County   \n",
       "136  50.489304  19.098381  05/30/2014 16:10                  Zendek   \n",
       "137  51.478199  -0.446081  05/14/2014 10:51                Hounslow   \n",
       "\n",
       "            country  \n",
       "133          Poland  \n",
       "134          Poland  \n",
       "135          Poland  \n",
       "136          Poland  \n",
       "137  United Kingdom  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pull row from full data set (df) where lat/lon match the lat/lon of each row of representative points\n",
    "# use iloc[0] to pull just the first row if there are multiple matches\n",
    "rs = representative_points.apply(lambda row: df[(df['lat']==row['lat']) & (df['lon']==row['lon'])].iloc[0], axis=1)\n",
    "rs.to_csv('data/reduced-set.csv', index=False)\n",
    "rs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "646\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# to demonstrate the data reduction, compare how many observations of 'Spain' in each data set\n",
    "print(len(df[df['country']=='Spain']))\n",
    "print(len(rs[rs['country']=='Spain']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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77ru4XC6WLl3aYXlJSQkul4vHHnusw/JNmzZxySWXMGDAAJKSkhg9ejQ33XRT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T/pJxaLqXmEiGxoClAiIiJyx0J1IdJn05i0odxbvqDb3pGts8v1roxdofNaJ62xVjLTGX5034+S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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x236e859e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the final reduced set of coordinate points vs the original full set\n",
    "fig, ax = plt.subplots(figsize=[10,7])\n",
    "rs_scatter = ax.scatter(rs['lon'], rs['lat'], c='m', alpha=0.3, s=200)\n",
    "df_scatter = ax.scatter(df['lon'], df['lat'], c='k', alpha=0.5, s=5)\n",
    "ax.set_title('Full data set vs DBSCAN reduced set')\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.set_ylabel('Latitude')\n",
    "ax.legend([df_scatter, rs_scatter], ['Full set', 'Reduced set'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Despite the massive reduction in data set size, our smaller set is still spatially representative of the larger set (until you get to very fine spatial scales, as determined by the DBSCAN epsilon value).\n",
    "\n",
    "If you're interested in cluster analysis and want to explore further, advanced topics include:\n",
    " - R-trees: https://en.wikipedia.org/wiki/R-tree\n",
    " - k-d trees: https://en.wikipedia.org/wiki/K-d_tree\n",
    " - OPTICS: https://en.wikipedia.org/wiki/OPTICS_algorithm\n",
    " - ELKI, a Java-based tool, lets you use DBSCAN explicitly with lat-long distances: http://elki.dbs.ifi.lmu.de/"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
